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This study presents an integrated approach for analyzing the impact of macro-ergonomics factors in healthcare supply chain (HCSC) by data envelopment analysis (DEA). The case of this study is the supply chain (SC) of a real hospital. Thus, healthcare standards and macro-ergonomics factors are considered to be modeled by the mathematical programming approach. Over 28 subsidiary SC divisions with parallel missions and objectives are evaluated by analyzing inputs and outputs through DEA. Each division in this HCSC is considered as decision making unit (DMU). This approach can analyze the impact of macro-ergonomics factors on supply chain management (SCM) in healthcare sector. Also, this method ranks the relevant performance efficiencies of each HCSC. In this study by using proposed method, the most effective macro-ergonomics factor on HCSC is identified as "teamwork" issue. Also, this study would help managers to identify the areas of weaknesses in their SCM system and set improvement target plan for the related SCM system in healthcare industry. To the best of our knowledge, this is the first study for macro-ergonomics optimization of HCSC.

a b s t r a c tThis paper introduces an approach based on stochastic data envelopment analysis (SDEA) for performance assessment of electricity distribution units. A new approach is applied for assessment of Iranian distribution units from 2001 to 2011 in this paper. There are usually incomplete and stochastic data or lack of data with respect to electricity distribution companies. Due to lack of information about some parameters, theory of probability is imported to the model. Different Iranian distribution units are considered as decision making units (DMUs). Network length, transport capacity and the number of employees are chosen as inputs while number of customers and total electricity sales are chosen as stochastic outputs. Then, the best electricity distributions units are selected with respect to efficiency scores in stochastic environment. Also, SDEA model is performed for each input, separately to identify the most important input indicators by comparing the results of associated efficiencies with SDEA model. The empirical results show that network length is the most important and influential input factor in this particular case study. To the best of our knowledge this is the first paper that examines stochastic outputs for assessment of electricity distribution units by SDEA in Iran.